Optimizing Energy Efficiency in Wireless Sensor Networks Using Dijkstra's Algorithm
Bibliographic record
Abstract
A Wireless Sensor Network (WSN) is a network of special systems containing separate sensors working together to collect data about a particular phenomenon without the need for human intervention.Because of the limited battery life of sensor nodes, energy efficiency becomes a significant issue in Wireless Sensor Networks (WSNs).This paper employs Dijkstra's algorithm to optimize energy-efficient routing in WSNs.Traditional shortest path finding algorithm in networks, Dijkstra's Algorithm is adjusted to minimize energy consumption by selecting routes that balance the energy load among nodes.This work employs the algorithm to consider dynamic aspects and energy metrics associated with WSNs.Using simulations, this proposed algorithm is compared against the Ant Colony Optimization algorithm (ACO) in terms of energy consumption, run time, network lifetime, node death rate, and data transmission success rate.The results show that Dijkstra's algorithm reduced overall power usage and extended network lifetime.This research emphasizes how graph-based algorithms can improve on-energy usage in WSNs thus providing a promising approach towards sustainable and long-lived sensor network deployments.Further optimization techniques and practical implementation scenarios will be explored in future work.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".